Merge pull request #498 from jungmannlab/development

v0.7.1

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.7.0
+current_version = 0.7.1
 commit = True
 tag = False
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?

changelog.rst

@@ -1,15 +1,22 @@
 Changelog
 =========
 
-Last change: 06-JUL-2024 MTS
+Last change: 19-SEP-2024 MTS
+
+0.7.1
+-----
+- SMLM clusterer in picked regions deleted
+- Show legend in Render property displayed rounded tick label values
+- Pick circular area does not save the area for each pick in localization's metadata 
+- Other minor bug fixes
 
 0.7.0
 -----
 - Adaptive Intersection Maximization (AIM, doi: 10.1038/s41592-022-01307-0) implemented
 - Z fitting improved by setting bounds on fitted z values to avoid NaNs
 - CMD ``clusterfile`` fixed 
 - Picasso: Render 3D, rectangular and polygonal pick fixed
-- picasso.localize.localize fixed
+- ``picasso.localize.localize`` fixed
 - default MLE fitting uses different sx and sy (CMD only)
 
 0.6.9 - 0.6.11

distribution/picasso.iss

@@ -2,10 +2,10 @@
 AppName=Picasso
 AppPublisher=Jungmann Lab, Max Planck Institute of Biochemistry
 
-AppVersion=0.7.0
+AppVersion=0.7.1
 DefaultDirName={commonpf}\Picasso
 DefaultGroupName=Picasso
-OutputBaseFilename="Picasso-Windows-64bit-0.7.0"
+OutputBaseFilename="Picasso-Windows-64bit-0.7.1"
 ArchitecturesAllowed=x64
 ArchitecturesInstallIn64BitMode=x64
 

docs/conf.py

@@ -26,7 +26,7 @@
 # The short X.Y version
 version = ""
 # The full version, including alpha/beta/rc tags
-release = "0.7.0"
+release = "0.7.1"
 
 # -- General configuration ---------------------------------------------------
 

docs/render.rst

@@ -21,9 +21,9 @@ Adaptive Intersection Maximization (AIM) drift correction
 
 1. In ``Picasso: Render``, select ``Postprocess > Undrift by AIM``.
 2. The dialog asks the user to select:
-   a. ``Segmentation`` - the number of frames per interval to calculate the drift. The lower the value, the better the temporal resolution of the drift correction, but the higher the computational cost.
-   b. ``Intersection distance (nm)`` - the maximum distance between two localizations in two consecutive temporal segments to be considered the same molecule. This parameter is robust, 3*NeNA for optimal result is recommended.
-   c. ``Max. drift in segment (nm)`` - the maximum expected drift between two consecutive temporal segments. If the drift is larger, the algorithm will likely diverge. Setting the parameter up to ``3 * intersection_distance`` will result in fast computation.
+|   a. ``Segmentation`` - the number of frames per interval to calculate the drift. The lower the value, the better the temporal resolution of the drift correction, but the higher the computational cost.
+|   b. ``Intersection distance (nm)`` - the maximum distance between two localizations in two consecutive temporal segments to be considered the same molecule. This parameter is robust, 3*NeNA for optimal result is recommended.
+|   c. ``Max. drift in segment (nm)`` - the maximum expected drift between two consecutive temporal segments. If the drift is larger, the algorithm will likely diverge. Setting the parameter up to ``3 * intersection_distance`` will result in fast computation.
 3. After the algorithm finishes, the estimated drift will be displayed in a pop-up window, and the display will show the drift-corrected image.
 
 Marker-based drift correction

picasso/__init__.py

@@ -8,7 +8,7 @@
 import os.path as _ospath
 import yaml as _yaml
 
-__version__ = "0.7.0"
+__version__ = "0.7.1"
 
 _this_file = _ospath.abspath(__file__)
 _this_dir = _ospath.dirname(_this_file)

picasso/__main__.py

@@ -505,15 +505,16 @@ def _smlm_clusterer(
     paths = glob.glob(files)
     if paths:
         from . import io, clusterer
-        if radius_z is not None: # 3D
-            params = [radius, radius_z, min_locs, 0, basic_fa, 0]
-        else: # 2D
-            params = [radius, min_locs, 0, basic_fa, 0]
-
+        params = {
+            "radius_xy": radius,
+            "radius_z": radius_z,
+            "min_locs": min_locs,
+            "frame_analysis": basic_fa,
+        }
         for path in paths:
             print("Loading {} ...".format(path))
             locs, info = io.load_locs(path)
-            locs = clusterer.cluster(locs, params, pixelsize)
+            locs = clusterer.cluster(locs, **params, pixelsize=pixelsize)
             clusters = clusterer.find_cluster_centers(locs, pixelsize)
             base, ext = os.path.splitext(path)
             smlm_cluster_info = {
@@ -858,10 +859,10 @@ def prompt_info():
             y_min, x_min, y_max, x_max = roi
             roi = [[y_min, x_min], [y_max, x_max]]
         camera_info = {}
-        camera_info["baseline"] = args.baseline
-        camera_info["sensitivity"] = args.sensitivity
-        camera_info["gain"] = args.gain
-        camera_info["qe"] = args.qe
+        camera_info["Baseline"] = args.baseline
+        camera_info["Sensitivity"] = args.sensitivity
+        camera_info["Gain"] = args.gain
+        camera_info["Qe"] = args.qe
 
         if args.fit_method == "mle":
             # use default settings
@@ -892,7 +893,7 @@ def prompt_info():
 
             try:
                 with open(zpath, "r") as f:
-                    z_calibration = yaml.load(f)
+                    z_calibration = yaml.full_load(f)
             except Exception as e:
                 print(e)
                 print("Error loading calibration file.")
@@ -924,7 +925,7 @@ def prompt_info():
             elif args.fit_method == "lq-gpu" or args.fit_method == "lq-gpu-3d":
                 spots = get_spots(movie, ids, box, camera_info)
                 theta = gausslq.fit_spots_gpufit(spots)
-                em = camera_info["gain"] > 1
+                em = camera_info["Gain"] > 1
                 locs = gausslq.locs_from_fits_gpufit(ids, theta, box, em)
             elif args.fit_method == "mle":
                 current, thetas, CRLBs, likelihoods, iterations = fit_async(
@@ -955,15 +956,16 @@ def prompt_info():
 
             localize_info = {
                 "Generated by": "Picasso Localize",
-                "ROI": None,
+                "ROI": None, #TODO: change if ROI is given
                 "Box Size": box,
                 "Min. Net Gradient": min_net_gradient,
-                "Convergence Criterion": convergence,
-                "Max. Iterations": max_iterations,
                 "Pixelsize": px,
                 "Fit method": args.fit_method
-
             }
+            localize_info.update(camera_info)
+            if args.fit_method == "mle":
+                localize_info["Convergence Criterion"] = convergence
+                localize_info["Max. Iterations"] = max_iterations
 
             if args.fit_method == "lq-3d" or args.fit_method == "lq-gpu-3d":
                 print("------------------------------------------")
@@ -1120,7 +1122,7 @@ def main():
     parser = argparse.ArgumentParser("picasso")
     subparsers = parser.add_subparsers(dest="command")
 
-    for command in ["toraw", "localize", "filter", "render"]:
+    for command in ["toraw", "filter"]:
         subparsers.add_parser(command)
 
     # link parser

picasso/__version__.py

@@ -1 +1 @@
-VERSION_NO = "0.7.0"
+VERSION_NO = "0.7.1"

picasso/aim.py

@@ -612,7 +612,7 @@ def aim(
     roi_r=60/130,
     progress=None,
 ):
-    """Apply AIM algorithm to the localizations.
+    """Apply AIM undrifting to the localizations.
 
     Parameters
     ----------
@@ -639,10 +639,21 @@ def aim(
     """
 
     # extract metadata
-    width = info[0]["Width"]
-    height = info[0]["Height"]
-    pixelsize = info[1]["Pixelsize"] 
-    n_frames = info[0]["Frames"]
+    width = _np.nan
+    height = _np.nan
+    pixelsize = _np.nan
+    n_frames = _np.nan
+    for inf in info:
+        if val := inf.get("Width"):
+            width = val
+        if val := inf.get("Height"):
+            height = val
+        if val := inf.get('Frames'):
+            n_frames = val
+        if val := inf.get("Pixelsize"):
+            pixelsize = val
+    if _np.isnan(width * height * pixelsize * n_frames):
+        raise KeyError("Insufficient metadata available.")
 
     # frames should start at 1 
     frame = locs["frame"] + 1
@@ -716,9 +727,9 @@ def aim(
 
     new_info = {
         "Undrifted by": "AIM",
-        "Intersect distance (cam. pixels)": intersect_d,
+        "Intersect distance (nm)": intersect_d * pixelsize,
         "Segmentation": segmentation,
-        "Search regions radius (cam. pixels)": roi_r,
+        "Search regions radius (nm)": roi_r * pixelsize,
     }
     new_info = info + [new_info]
 

picasso/clusterer.py

@@ -299,7 +299,9 @@ def cluster_3D(x, y, z, frame, radius_xy, radius_z, min_locs, fa):
     return labels
 
 
-def cluster(locs, params, pixelsize=None):
+def cluster(
+    locs, radius_xy, min_locs, frame_analysis, radius_z=None, pixelsize=None
+):
     """
     Clusters localizations given user parameters using KDTree.
 
@@ -309,8 +311,15 @@ def cluster(locs, params, pixelsize=None):
     ----------
     locs : np.recarray
         Localizations to be clustered
-    params : tuple
-        SMLM clustering parameters
+    radius_xy : float
+        Clustering radius in xy plane (camera pixels).   
+    min_locs : int
+        Minimum number of localizations in a cluster.   
+    frame_analysis : bool
+        If True, performs basic frame analysis.
+    radius_z : float (default=None)
+        Clustering radius in z plane (camera pixels). Only used for
+        3D clustering. 
     pixelsize : int (default=None)
         Camera pixel size in nm. Only needed for 3D clustering.
 
@@ -322,12 +331,11 @@ def cluster(locs, params, pixelsize=None):
     """
 
     if hasattr(locs, "z"): # 3D
-        if pixelsize is None:
+        if pixelsize is None or radius_z is None:
             raise ValueError(
-                "Camera pixel size must be specified as an integer for 3D"
-                " clustering."
+                "Camera pixel size and clustering radius in z must be specified"
+                " for 3D clustering."
             )
-        radius_xy, radius_z, min_locs, _, fa, _ = params
         labels = cluster_3D(
             locs.x,
             locs.y,
@@ -336,17 +344,16 @@ def cluster(locs, params, pixelsize=None):
             radius_xy,
             radius_z,
             min_locs,
-            fa
+            frame_analysis,
         )
     else:
-        radius, min_locs, _, fa, _ = params
         labels = cluster_2D(
             locs.x,
             locs.y,
             locs.frame,
-            radius,
+            radius_xy,
             min_locs,
-            fa
+            frame_analysis,
         )
     locs = extract_valid_labels(locs, labels)
     return locs
@@ -378,7 +385,7 @@ def _dbscan(X, radius, min_density):
     return db.labels_.astype(_np.int32)
 
 
-def dbscan(locs, radius, min_density, pixelsize=None):
+def dbscan(locs, radius, min_samples, pixelsize=None):
     """
     Performs DBSCAN on localizations.
     
@@ -389,7 +396,7 @@ def dbscan(locs, radius, min_density, pixelsize=None):
     radius : float
         DBSCAN search radius, often referred to as "epsilon". Same units
         as locs.
-    min_density : int
+    min_samples : int
         Number of localizations within radius to consider a given point 
         a core sample.
     pixelsize : int (default=None)
@@ -411,7 +418,7 @@ def dbscan(locs, radius, min_density, pixelsize=None):
         X = _np.vstack((locs.x, locs.y, locs.z / pixelsize)).T
     else:
         X = _np.vstack((locs.x, locs.y)).T
-    labels = _dbscan(X, radius, min_density)
+    labels = _dbscan(X, radius, min_samples)
     locs = extract_valid_labels(locs, labels)
     return locs
 

picasso/gui/localize.py

@@ -1221,10 +1221,10 @@ def init_menu_bar(self):
     @property
     def camera_info(self):
         camera_info = {}
-        camera_info["baseline"] = self.parameters_dialog.baseline.value()
-        camera_info["gain"] = self.parameters_dialog.gain.value()
-        camera_info["sensitivity"] = self.parameters_dialog.sensitivity.value()
-        camera_info["qe"] = self.parameters_dialog.qe.value()
+        camera_info["Baseline"] = self.parameters_dialog.baseline.value()
+        camera_info["Gain"] = self.parameters_dialog.gain.value()
+        camera_info["Sensitivity"] = self.parameters_dialog.sensitivity.value()
+        camera_info["Qe"] = self.parameters_dialog.qe.value()
         return camera_info
 
     def calibrate_z(self):